{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-17T18:21:47.017801Z",
     "start_time": "2017-05-17T18:21:46.989430Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "from __future__ import absolute_import, division, print_function\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import GridSpec\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os, sys\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set_context(\"poster\", font_scale=1.3)\n",
    "\n",
    "import missingno as msno\n",
    "import pandas_profiling\n",
    "\n",
    "from sklearn.datasets import make_blobs\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Uncomment one of the following lines and run the cell:\n",
    "\n",
    "# df = pd.read_csv(\"redcard.csv.gz\", compression='gzip')\n",
    "# df = pd.read_csv(\"https://github.com/cmawer/pycon-2017-eda-tutorial/raw/master/data/redcard/redcard.csv.gz\", compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_subgroup(dataframe, g_index, subgroup_name, prefix='raw_'):\n",
    "    save_subgroup_filename = \"\".join([prefix, subgroup_name, \".csv.gz\"])\n",
    "    dataframe.to_csv(save_subgroup_filename, compression='gzip', encoding='UTF-8')\n",
    "    test_df = pd.read_csv(save_subgroup_filename, compression='gzip', index_col=g_index, encoding='UTF-8')\n",
    "    # Test that we recover what we send in\n",
    "    if dataframe.equals(test_df):\n",
    "        print(\"Test-passed: we recover the equivalent subgroup dataframe.\")\n",
    "    else:\n",
    "        print(\"Warning -- equivalence test!!! Double-check.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_subgroup(filename, index_col=[0]):\n",
    "    return pd.read_csv(filename, compression='gzip', index_col=index_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clean_players = load_subgroup(\"cleaned_players.csv.gz\")\n",
    "players = load_subgroup(\"raw_players.csv.gz\", )\n",
    "countries = load_subgroup(\"raw_countries.csv.gz\")\n",
    "referees = load_subgroup(\"raw_referees.csv.gz\")\n",
    "agg_dyads = pd.read_csv(\"raw_dyads.csv.gz\", compression='gzip', index_col=[0, 1])\n",
    "# tidy_dyads = load_subgroup(\"cleaned_dyads.csv.gz\")\n",
    "tidy_dyads = pd.read_csv(\"cleaned_dyads.csv.gz\", compression='gzip', index_col=[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining and further considerations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_players = load_subgroup(\"cleaned_players.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = tidy_dyads.reset_index().set_index('playerShort').merge(clean_players, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373067, 10)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>redcard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>refNum</th>\n",
       "      <th>playerShort</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>lucas-wilchez</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>john-utaka</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aleksandar-kolarov</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alexander-tettey</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           redcard\n",
       "refNum playerShort                \n",
       "1      lucas-wilchez             0\n",
       "2      john-utaka                0\n",
       "4      aaron-hughes              0\n",
       "       aleksandar-kolarov        0\n",
       "       alexander-tettey          0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy_dyads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total redcards given</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>refNum</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total redcards given\n",
       "refNum                      \n",
       "468                       49\n",
       "432                       49\n",
       "449                       47\n",
       "916                       42\n",
       "2073                      36"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tidy_dyads.groupby(level=0)\n",
    "           .sum()\n",
    "           .sort_values('redcard', ascending=False)\n",
    "           .rename(columns={'redcard':'total redcards given'})).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total redcards received</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playerShort</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cyril-jeunechamp</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sergio-ramos</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gary-medel</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ballesteros</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>juanfran</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total redcards received\n",
       "playerShort                              \n",
       "cyril-jeunechamp                       19\n",
       "sergio-ramos                           19\n",
       "gary-medel                             16\n",
       "ballesteros                            15\n",
       "juanfran                               15"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tidy_dyads.groupby(level=1)\n",
    "           .sum()\n",
    "           .sort_values('redcard', ascending=False)\n",
    "           .rename(columns={'redcard':'total redcards received'})).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>redcard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>refNum</th>\n",
       "      <th>playerShort</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>lucas-wilchez</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>john-utaka</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aleksandar-kolarov</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alexander-tettey</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           redcard\n",
       "refNum playerShort                \n",
       "1      lucas-wilchez             0\n",
       "2      john-utaka                0\n",
       "4      aaron-hughes              0\n",
       "       aleksandar-kolarov        0\n",
       "       alexander-tettey          0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy_dyads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_ref_games = tidy_dyads.groupby(level=0).size().sort_values(ascending=False)\n",
    "total_player_games = tidy_dyads.groupby(level=1).size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_ref_given = tidy_dyads.groupby(level=0).sum().sort_values(ascending=False,by='redcard')\n",
    "total_player_received = tidy_dyads.groupby(level=1).sum().sort_values(ascending=False, by='redcard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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vMW4Dh6x/9yjN+M4mnk/YFN6R37oXj44xALQyRJGr3MvIFi9c6fUp+7l1vNPBPYl97V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\nMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e92adb898>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 758,
       "width": 1091
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(total_player_received, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zJzcx997lXStmGGx5oZklRDmfnbzLyysZzbvETEAcCBJT+abU1380KSRkRE7EixLFzr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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e92adbd68>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 758,
       "width": 1107
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(total_ref_given, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>redcard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playerShort</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cyril-jeunechamp</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sergio-ramos</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gary-medel</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ballesteros</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>juanfran</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  redcard\n",
       "playerShort              \n",
       "cyril-jeunechamp       19\n",
       "sergio-ramos           19\n",
       "gary-medel             16\n",
       "ballesteros            15\n",
       "juanfran               15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy_dyads.groupby(level=1).sum().sort_values(ascending=False, by='redcard').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "redcard    3092\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy_dyads.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(redcard    3092\n",
       " dtype: int64, redcard    373067\n",
       " dtype: int64, redcard    0.008288\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy_dyads.sum(), tidy_dyads.count(), tidy_dyads.sum()/tidy_dyads.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "player_ref_game = (tidy_dyads.reset_index()\n",
    "                               .set_index('playerShort')\n",
    "                                       .merge(clean_players,\n",
    "                                              left_index=True,\n",
    "                                              right_index=True)\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>refNum</th>\n",
       "      <th>redcard</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>skintone</th>\n",
       "      <th>position_agg</th>\n",
       "      <th>weightclass</th>\n",
       "      <th>heightclass</th>\n",
       "      <th>skintoneclass</th>\n",
       "      <th>age_years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playerShort</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>Defense</td>\n",
       "      <td>low_weight</td>\n",
       "      <td>mid_height</td>\n",
       "      <td>(-0.001, 0.125]</td>\n",
       "      <td>33.149897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>Defense</td>\n",
       "      <td>low_weight</td>\n",
       "      <td>mid_height</td>\n",
       "      <td>(-0.001, 0.125]</td>\n",
       "      <td>33.149897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>Defense</td>\n",
       "      <td>low_weight</td>\n",
       "      <td>mid_height</td>\n",
       "      <td>(-0.001, 0.125]</td>\n",
       "      <td>33.149897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>Defense</td>\n",
       "      <td>low_weight</td>\n",
       "      <td>mid_height</td>\n",
       "      <td>(-0.001, 0.125]</td>\n",
       "      <td>33.149897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaron-hughes</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>Defense</td>\n",
       "      <td>low_weight</td>\n",
       "      <td>mid_height</td>\n",
       "      <td>(-0.001, 0.125]</td>\n",
       "      <td>33.149897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              refNum  redcard  height  weight  skintone position_agg  \\\n",
       "playerShort                                                            \n",
       "aaron-hughes       4        0   182.0    71.0     0.125      Defense   \n",
       "aaron-hughes      66        0   182.0    71.0     0.125      Defense   \n",
       "aaron-hughes      77        0   182.0    71.0     0.125      Defense   \n",
       "aaron-hughes      77        0   182.0    71.0     0.125      Defense   \n",
       "aaron-hughes      77        0   182.0    71.0     0.125      Defense   \n",
       "\n",
       "             weightclass heightclass    skintoneclass  age_years  \n",
       "playerShort                                                       \n",
       "aaron-hughes  low_weight  mid_height  (-0.001, 0.125]  33.149897  \n",
       "aaron-hughes  low_weight  mid_height  (-0.001, 0.125]  33.149897  \n",
       "aaron-hughes  low_weight  mid_height  (-0.001, 0.125]  33.149897  \n",
       "aaron-hughes  low_weight  mid_height  (-0.001, 0.125]  33.149897  \n",
       "aaron-hughes  low_weight  mid_height  (-0.001, 0.125]  33.149897  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_ref_game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373067, 10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_ref_game.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bootstrap = pd.concat([player_ref_game.sample(replace=True, \n",
    "                                              n=10000).groupby('skintone').mean() \n",
    "                       for _ in range(100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>refNum</th>\n",
       "      <th>redcard</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age_years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skintone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>1536.734111</td>\n",
       "      <td>0.007509</td>\n",
       "      <td>182.517649</td>\n",
       "      <td>76.391410</td>\n",
       "      <td>28.044783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.125</th>\n",
       "      <td>1488.991157</td>\n",
       "      <td>0.007437</td>\n",
       "      <td>182.733940</td>\n",
       "      <td>76.111562</td>\n",
       "      <td>28.200370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>1494.372655</td>\n",
       "      <td>0.008767</td>\n",
       "      <td>181.543329</td>\n",
       "      <td>76.331805</td>\n",
       "      <td>29.242037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.375</th>\n",
       "      <td>1333.062447</td>\n",
       "      <td>0.011478</td>\n",
       "      <td>181.335003</td>\n",
       "      <td>76.489110</td>\n",
       "      <td>28.541058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>1548.482286</td>\n",
       "      <td>0.008091</td>\n",
       "      <td>180.952517</td>\n",
       "      <td>75.324107</td>\n",
       "      <td>28.122202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.625</th>\n",
       "      <td>1568.964821</td>\n",
       "      <td>0.006032</td>\n",
       "      <td>182.409178</td>\n",
       "      <td>75.025144</td>\n",
       "      <td>27.082485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.750</th>\n",
       "      <td>1582.911664</td>\n",
       "      <td>0.008640</td>\n",
       "      <td>180.800269</td>\n",
       "      <td>76.150315</td>\n",
       "      <td>27.329338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.875</th>\n",
       "      <td>1626.739051</td>\n",
       "      <td>0.008255</td>\n",
       "      <td>182.702591</td>\n",
       "      <td>77.654391</td>\n",
       "      <td>28.034831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>1581.547413</td>\n",
       "      <td>0.009814</td>\n",
       "      <td>181.931953</td>\n",
       "      <td>76.135947</td>\n",
       "      <td>28.397668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               refNum   redcard      height     weight  age_years\n",
       "skintone                                                         \n",
       "0.000     1536.734111  0.007509  182.517649  76.391410  28.044783\n",
       "0.125     1488.991157  0.007437  182.733940  76.111562  28.200370\n",
       "0.250     1494.372655  0.008767  181.543329  76.331805  29.242037\n",
       "0.375     1333.062447  0.011478  181.335003  76.489110  28.541058\n",
       "0.500     1548.482286  0.008091  180.952517  75.324107  28.122202\n",
       "0.625     1568.964821  0.006032  182.409178  75.025144  27.082485\n",
       "0.750     1582.911664  0.008640  180.800269  76.150315  27.329338\n",
       "0.875     1626.739051  0.008255  182.702591  77.654391  28.034831\n",
       "1.000     1581.547413  0.009814  181.931953  76.135947  28.397668"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_ref_game.sample(replace=True,n=10000).groupby('skintone').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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CBvhWYDTG+P4W9nsm8JvA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hqYNL9FqqOzsnbT8SntDXB7kyjUtuzhheJipaFkPHz952bLJvCAuXEc57qrlXssbm9G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sP73HzJ//HTeApnA0a9F5+c3RGIOxh2rmnix7/Z2Ij7LXDEd9B7YXrBQlReMKV5Os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Qi990HZeVWc9t/dsNAbelBVXnrdoKl+4jZSJuwY5oTVYSTw9myMh9u10mY9QGvubeQ6oozI\n/Y9IWbRvS4Ux3aGH9jBAw9bx8UFrysFs5IYIY4HDszEOdqfXH8eLcHg2ghfE8IMYQkpEkcCTvQbq\nlraUgOa878Hxopmvu0FcunOPYoGxlwxkXOYYLq65RcETAPS+O8dZ10Fn4JdGkOSh84kgCIIgLsdN\nrlNUQU5ABEHcV0goRBAEQRD3hMtOdMzENLELR59VHCHSQne+iKuqHOrkx/rjAN+/6eNgt46apeG0\n4+CDbSubFP3heDjjZKRyjr1NG9tNE44XwQ9jaArHwAlRtzRst4y5N56MAQpnOG47M0INpcSZZp5b\nzarOTcTyCCHxYMteyoHnfTgypIUEReE47bnYqpiCKmuuTBVOUG3FnD9/znvulbkArSpoIwHc6rwv\n16Z1IvR+LJYRD9HkHnGbqRJ2xLEsPA447jh4+rBRepzTmnu7mBdlZGgKHmxYCMIYnaGPsRuiZmnY\nahrYbVlz1zpy/yNSqtYEKSXOex7eno0QRgJDN4SuKpBS4s3JcMrBLB1+kJA46Th4+rAJsOQ5Xp8M\n0Rn4M25AR+0xVM7QsHXEIvm5n3+8lQyUSKA98LNrd3vgQRbWviiWWdxY8X1HscDQCXB4Ns7uvc57\nLnY37aX/XYprbip4KrYkJSRenQyhcA73bISD3TpsQ8XIDTFyQwghwSfvIRVG0flEEARBEOtzm+oU\nBEEQ9xkSChEEQRDEPWGdiY6yBu6zR0lRud3313KEiGJRWcRNCSOBV8cDfLjfRBiJqUnRMjefSAj0\nRj4Odut4uFMDAAzHAf7wog1VYXObzUEo4AcChiamBEgDJ4CqcDSsWfFGveRrKTvvKdrnPnLe9yYx\nX/MdePKPv+4C/2Wm/VctnFylC9BlogiJ5biKz2tZEc1ttsqmyT3iLlEl7FCUcjHQ2I1KRaK05t4e\n5kWDOV6El0cDfPXNGfgkFjdFUzj2tmxsNYzKJsnS1xGZuMV89e0ZHu3Ubs36TyxP2ZrgeBFenQzR\n7ruQMhHl+EGMMBaIpUTdTqJvUwez/PEXxiKLUj7veTjvuQjjxEVIQoKBQeEcfhDh3AlxdO5go2FA\n1zi+ftHByE2ixPJDIsdtB2MvyvYgAGZiQYBE+B9GApwxSMkyV6GBE+Af/+c7/O+/+mDppmF+zZVS\n/v/s3WtzI9edJvjnnLzjRhIki6yiVDfJkqzLOOx2t92zE9Ebsx3zYl7sZ/J8pX3RERux29uzMzFu\n97rlcctWS2qpSlUSq3gFQVwykdezLxKJSgCJGwmyQPL5dXSExUoCIJDIPHnyOf8/DhruxOvLrhel\nlWVNDS8P2zB1iWjkurLlBoPvJiu7ERERXc5NnqcgIrorGBQiIiK6QxYJJkxbHT0aYFikIoQmxdRJ\n3IxSwP5xB++/s4Z/ffEmiDGpmo/C8Or8aslEvWaj44UTnyMIEzQ7PWyuO7AKJoujOMFZu4dEvXle\nQ5MTg0Lz9tami8lWUwshJlbgKdr+Kl12tf8iEyfLrAJ0mVaENJ/Lfl6LHIOB1QzcsFoQ3TWTgh3V\nkolm2x/7eXajfhSPuTfDtLBwGorvwg9jNNs9SCmGjn1ZCygAg6qD6xVz6Jj5w1EHcaLSceeEQ6bb\ni3DYcBHGCcq2AdPQ0PFC/M9vT6BJgce7NTy5X+Vx94YbHbe5vQg/HrVx1uoh2/3yoRxNCDTOPdTX\nHIh+BbPtdXtoUUTHC1G2dXzzQxOnbR8qUYAQkFJAkwB6aaVXx9SgaQKtboBaycCLwzZq5TSEtJ9V\n57F1VBwDJVtDo+3DNtNw02i74CRR8MMYSgFCSxeltDoBLFODaUh4QYT/8afXaRWuKJlZgSt/zO16\n0dSxV6LSIJUQAs22j1rFhG1qY9tl301WOCAiIrq8VZynICKiNxgUIiIiumPmCSZMWx0NjLdSWuTC\nT5Ni7hvoYZSg5Ybwg2jws4pjjLUeywytzhfAw50q3F6IlhuOPadSCh0vxEbFxqOdKgAM2qgN6VdW\nAgABgZ16qfBmzSr11r6tVrEKzjKqxsz7/Vnm33/ZVoQ022U+r0WPwato0aAT0W0waXxTLRswdIlw\npLpGXFAlkcfcm2NiWFgBhw0XiUrQ6gZQSD9rP0yGgglZC6jS/Ro+/+YYJUuDn9tHzrvBUIWT0fa+\nWYVOz0+rLB4mLr7bb0LXJCqOCcuUaHZ8HJ97UysX0erLj9uyyjm9MBk6hmTXMFII6LqEAtBs+1gv\nG4hiBah0rJEdp9xeiN9/3cbr0y5ipfqXNyJdrSGAQIshpYQfxrAMDZoEPD9EtV+pKH3ONws10uqe\nJsp2ALcXQbMFRO6iSSmFIIz7rRgFDEsiUQpeEMELIigFnJsBztoBvnjWwEbVHFxXTarAlT/mTlsc\nkr0viVL972QaWCoKCmXv5euTLpRSvLYjIiIiIqJbi0EhIiKiO2haMOEyrZTmESsFQ5MI5wgLGbrE\nadMb+lnFMab+fn51fr1q4dcf7+Drl2c4bflDraqESCeddzfLgxsve9vlwarsPE0K7Gw4qDhmYfsQ\n3vS+HqtYBWeZVX5mWebff5FWhLSYi35eV30Mvg63IehEdBGTAoICAnvbFXx/0EI+j6yNVEnkMfdm\nmRQW7nghwjhBECaIkzfn/aJgQhgnOG56aHYC3N8sDVWYysLxWYWTve03Y1Yo4OVhG0dnHrq9EHGs\noJSC7I9Tz7tptZh6zcJBw4Vjafji2Sk+fVJHkihWerth8uO2bAGEPxJSExAQQqFkp/tQGCZw4whh\nGMHUtTRss+Hg1UkXbS/EWctHtxciUYBK0mu0LGykCcCLY0gRQwiJOE5g6BKqvzuXbAOm0W8vlluo\nsVsvwfMjJMqDUmlgKYjj/nYKYZy2NXMsDRX7zb4exwquHyEI4zRkpImhYF1RBa6NqjV0zC0KXuZZ\npoa2Gw6+k2rGOMs0tLFKoERERERERLcJg0JEREQ05LKtlGaJY4Wdeqm4ek+eAPa2KwjCBLoc/vm0\n388mibObbRtVC7/+ZHesgtJ5x4cXxEPVgUq2jif3a+h4ITpeiCRRkFKg4hh4b6+Gjx5usI3OW7SK\nVXCus8rRsv/+RVoR0uIu+nld9TH4qt2GoBPRRU0LCJYdA493a9g/7iDoF0bMtzLlMffmmRT+zSqb\n+MHwsbwomKCUwo/HHZQdY6wVXdnRBxVgsupDT+7XAAEcNz0cnLpw/RAKaZDCzA2YE5WkY1mlsIW0\nLZOUAn/32xd4515laPzLSm83QzZu+38+/xHA+P5kmhKmkoAC2m4wOA/HcYLYVDg+96AAOJaGZ69a\nkFIgSdJKQolKr6pE///jWEEJABCQSBDF6Q6jSQEhZVqpqGoNwkLZvptdS2lSwPUjGFLgqJkgihP0\nYgVdStj9kFB+8YUXxBAi3Y/DOEHFMcaCdfkKXNn4IX/M1WRaBdYPk357s7QakGVosAwJ05DQpED2\ntZ029tA1ibKjF1YCJVolbPNLRERERJfBoBARERENWUYrpWl0TaJk6xOr9wBpJaG97QrKjgE/iBFH\n0dC/T/t9TQqEYYz1io394w4OG+5gsiz/Ov/07BRe0c14AVRKxtCNGiCdMGdv7bdrFavgXGeVo6v4\n++dpRUgXc9HPa+gYrFAYXKw4xuAm76rdxLrpQSeiy5gVECw7Bn7ycB1HJ130/AiPdiowdI3H3Btq\nUvg36YfWR4/9RZ9vECaDlnSjFVGEENitl7B/3BmEgTpeiIpj4IejdtquCUCSALomxlrjKih4foxW\nN8Bx0+sHjjAWSAJY6e2m2Kha+Mk7azAPNURRAqh0HGGZGkxD4ujMQ6cXAkohTtL/lwCg0mukIIzw\nw6EH20orDPlBhCAChHgTEkrSLmVQKt0nlUwrFcVJ2oIsDGPEUYJYJditlyCEGNp3S7aOjx5uQNcF\nDCnx40kXLw5akNKHJgUMffh7E8UKSiUwdA1BlB47w1jBKhg7hVGCozMXSf+11asWwjCGoWvQpMDp\neW/se9TzI2hSoF6zsb1u49VJFwqAZRS3HRPA4O+6TCVQuhluctCGbX6JiIiI6LIYFCIiIqIhV91K\nKQtWTKreUyubuLdVhujf7Xj/3XV8/fxk7HGKfj+ME1iGhK5LdP0QXT9d0V00WTa1skvBzXnH0AYr\nU+ntWWYVnGVMDC+zys88r+cqqgAxAHd1LvJ5ZcdUtxcVhiFbbpC2D6mXULL1lbuJddVhU6JVNk9A\nUCAd6/z1J7u8gXfDTQoLy35LOTkyligKJvhBPNhutBUdkIXjKzhouIj6QSEAcP0IiVKQQkDTBGJV\nfC5IVIJeEOOg4Q7GIEVBoXRbVnq7CQxdQ6Vk4OFOFa9Ou4OfZxWGkkQh6FfUAQAlBII4hhYIHDZ6\n8IIYJSutVmWZOvwogRQSUaKgFKBUMggKZY+r+uGhMErg9iIIAbg9gShKsLNRQq1kDr9IAWzVHHz0\naAO/AvD/fXWE//4vrwb7b16SJChZxiAwB6RjodF9MAgTtLoBWt0AtYqJH446/QpECs9enfXHSwIo\nqDibJOmiD2lKrFdtdLwQljF+LahrErv98VX233R73eSgDdv8EhEREdEyMChEREREQ6ZNiCql0PUi\ndLwQcaKgXSBAMxSsKKjeo0kxCAnZlo6fPt7A9/tNuL3xieX877u9CEdnLtarVuFrGZ0sm3RzZ9LN\n+ePzHn735eFKTxjeFcuogrOsieFFq8YAwEnTG3vdUgp880NzrtfDKkA3y6Kfl65JuL1oanvGME6w\nf9zF3nYZ+sbV3sRaNFB31WFTolU3MyBo6fhgr4aqMx7UoJtlUli44hhouQEsU4MXpFUxNSkKgwmJ\nUnAsffB7RdJwfBVdLxqEiXRNwrEMGJqA50eIp+SV/TAeCiElyeTxymilt5tcbeO2yq5h8q3pgDRI\nU/TZalJACgFdEwiiBEql+0AUK0Cp9LpLphPEQZRg9CFU1pMM6f4aRHG/BZlA24sQRl2sVayxhRaO\noeGk6WFzzYZjavjJO+t4/roFP4gHITfL1OD5EfwwRhCpoSfNB+uCMEGz7UNBIUl7og0qBzmWBgWg\n7YZwLA1dLxkaPWlSolY2YRgSnh/B1AU+e1qH6r/e7Jq24hgoO/rQfn2ZSqC02s7aPv7luxO03eLK\nnUVBm1U5HrLNLxEREREtC4NCRERENGRagCZbzZy3aIBm3mCFEAKfvbcJKSV++mgDf/jmePL2Cjhq\nuLjXLxM/SX6yrOjmzqSb87omUXZ0rsxbIZepgrPsFZjzVo0BgN99eTi2zbPXLRydubi38WYF86zX\nwypAN8sin9fmmoXf/tmdGBLKKCgcNlz85Ufby3qZYy4SqFt09T1X69NtlA8InnV82I4FQ5e4Vy/h\n3oaDZtNN2wbRjTZpTFtxDBiahDIUNCmRqAS1slk4RjV0CcvQBmPNac9VKRnY3SghihO8OukOqsmI\n0Z5jI7Ib2hlZULkoL6v0dpOrbdxm+WuYfGs6P4jh+TGkBGxLQxwrKKVg6hJVx0SlZMDzIzS7Abwg\nhmlIKACmLtOWXyKrhJW2HxutzZMu5kh/FicKuhCAUlBQeP76HF0vhNTeBNncIMKfvm/ANjRo/f27\n7BiwzOHKWn6Q7l/5/VhK+SZYp4BWNxiMi0YrcHW9CFIK1GsWkn47spOWD5UoWKaWBo76D22ZGjZr\nFsr9UF5RZa3MrEqgdHMppfD5N8d4edgurNypaxJVx0CcpGPtT5/UYRoajpoe/BU4HrLNLxEREREt\nC2dliYiIaEg2+ZyXBmg6YyEhQ5NDK+7O2v5cz5EFK0afJ2NbOn7503vYXHPm2j6MYtyrF4csRmWT\nZdnNnUFbCAUcNsZvzgsAu7kAUhY2UjNW8NFqWnQF5ryfc3ZT+NPHdexulLBVs7G7UcKnj+v41cc7\nAIAvnp2OT+r297sgSrB/3IHbi5byeugGU0VNMyZuOv/GC8oCdZNuREw67i+6+p6r9em2ygKCP31c\nxy8+uofP3t/CzoxAM908hWNUAezUS5BCol6zsFGxYBaMYQUE3tmuQIjhseY0W2s2dE2i5OjQZDql\np+vTp/akEDBz1YwmVS7KRHFy4XMAXb38NUzWmk7XJPwwHhrf6ppAfc3BO9sVbK7bsEwNQgroWhpe\nS1Q//CMBU9cGrcYgBKQUGOTJRLoPSZEGeIB06JEoBQgBQ5PoeCEOmy6gxq+demGMozMXnp8Gm0b3\n8iw4lO3HAsBG9U2wzg9jxEkytn22H2ftzIQQ0DSJzTUHf/HBNj56tIHtdQcVx0CtZOLBZhlP79fw\nYKsy1hZwVFYJlMfr2+nZqxaevWqNhYSAtHrVwamLr1+e4aQf9v3T8wb+/vMf8dWLs8Jrtes+Hl6k\nzS8RERERURHtN7/5zdt+DUR08/wGAHq9cGrZciK6mYQQKDsGjpseFNJgxY/H3bFghYDA/a0yDOPN\nhPF5x8fednmuSVXH0rG3XUbVMSCFgG1qqJVMPN1bwy9+uoOS/eYmRq8XwjK0wu0f71ahaxLBAivz\nZf/mnWPpqJVNnHd8NDsBzrvB0Ha6JvFgqzwWQIoShapjDL1GWl1ZmfiXhx1880MTLw87ANKbItMW\n4S/6OQshULINbK07uLdRwta6M/jdP357UjgZ3fHCof3O9SOsV4qrDnC/Wy4pBRzHHPrZKoxtfjjq\nIIwTtN1g6nYCwIOtMmxTX/oqYaXUxH12aDuMH/cdS8dhw0U0x/toGxp+8u46b8TRrbaqxxpanqIx\n7WbNxvt7a6g6BmzLgOtHQ2NpQ5O4v1XGTr2ESkGVlSKDYyaA42YPXhDDD9JqKmGUVnaBAqJYwQ8S\nRFGCKFIo2TrWyxZ0XcLQJHY2SkPjn6y1cKPl47wbIE4SvD7tQghMPT4vOvan5clfwwgpsF4xcdrq\nwQ9j6JqEqWvY2Sxhe80ZqwwURjHCKIEuBWoVC10v7JcPSj/DN6F0AYm00JAmBSAENCmhpYWEICBQ\nKxmIkv4yCyVQLZl4d6c6du1k6BJHjX4bMksf+j7oUqIXxBACiGOFsq2jXnvTysn1osFiFU1KVEsG\nDF3DvQ0HQgicd9T02eUAACAASURBVAOEuevArPqWaWiolAxUy+bgvyGAtZKJ999Zx3nHLxyr2IaG\njx/XWS3rllJK4R/+sA/Xj8Z+3vEiHDc9BGGMMEnb7JVtHactH6ahIVGq3+JOhzES0LzO4+FBwx17\n/dPYpoZ7G6WlvgaObYjouixyvMnP/R00XDRaaYDTsXSOVYloqpFjzX95m6/lurH1GBEREY3Jt1I6\nOe8VVhLaKajgs2hp66J2PLouJ17ATWrfc9AYb5U2Tf7vySrB/NOXh+h4IeJEQZMCFcdA2Zl8MXnC\nEt43wmjbjINTFy03QMsNJu7Hecv4nKeVh89WQWeiOEHXiya2QuB+d/tFcTKoEFDU7hFIQ4y7/X23\n6N8v6zItDeZtL8nV+kR0m0waoz59UMPpeQ/HTQ+Nto+uF6LsGKjXLGyvOdhcs9HsBAsdMzfXbGzW\nLBw1PQA2Wt0AjqnhvBujF8aDoIdAWlUmCBO8Ou1iZ7OED9/dGAoJFbUWNjSBZjcYOtdMwrY2b092\nDXPS9PDsVQu2KWHqEqV+BR274KaYZUhoUsAx06BD1THQdUx0exGACFITkEpCQUElKq0s1A8JKaWg\nawJCCBgivR4s2QY6vRAaBBxbw3rFLNxfhBCor9no9tL9/8n9KrpeNLj2Mg0NbTfARsUaC6gluf25\nVjYhhRiqWKSNtNKbFVTQNTnUHjK71tU1ia01G5trNscmt9jpeRqyzAvCBK1uumgoUW+OhWEUI4oU\nHEsfVLDKWv8+uV8bW3ByXcdDtvklIhrHlrlERBfDoBAREdEdk62wmDUpOhqgSfqTxRXHSCfKJsyf\nvo0gw2Uny7IqSrub86+0u4qb87RcWduM/I23OHfzIIwT7B93sbc9XjUqs4zPeVq596KbGR0vnBgU\n4n53+2XHp5Ktj91IKwoxXsXk/0VaGuSP+/mwaVHgiBN1RHRXTAoQ5S16zBRC4KNHdZx3A/xw1MHm\nmoVm28dZJ4EEoISA6LeLsgwNZUeHHyZodQJ4fjQY82SthfMjEUOTg7FJFKdtUfe2K1ceqqaLaXYC\nfPeqhV4YY6Nio9lJxwtnHR+aG2K9Yg1dsgkhUCubaHVD7G2VkCigXrMhRXreD4IYQleIkwSWpSNO\n0hZlAoCp6xD9IUfaHsyCrmso9cfZtqFjWofckq2jbBmIkwS9MEalZAyNd+NYAVDww2QovCb7lYxq\n5TSENBpeqzgGWrkqjFJOD/lkLU/n+W7S7XNy3hsKlwVhgma7hzBWQyGhjOdHSJIEtbI5aOEYxsnE\n67WT8x421+xBgK/Rb0dWr1p4ulfD1ppz6SDa1pqNgzN3UA1u1mIntvklordh3rnnZSia+8vLWkR+\n9nSTcxBERCMYFCIiIrpDFl1hcVMCNNlk2SLbj+LKvNtFKYWvX56NTRSMrjqetioUWM7nPO07UXQz\nI56yEpr73e2XP55l7TMmBcey7Zdt0eN40fZcrU80v+ucSKfVtOgxM91+F4Z+hO8PWjjrBDANDaYB\nxAkApWAZaZve7Bxy3vHx7Y/nqFctQKQVOfMjDgGBnXoJzf6NbSBtp3PQcPHkfnXivsgQ89sxelOs\nUjKxVjbR9kIoAHGS4LTVQ83RoefaJFmGhg8fllHrt/DqeCFOSwZen3TR8kJYetreq+1FaLZ8KCQw\ndW0QEpIirUhUdkz0gjftjyxTmxnScSwNnzzZnrifAxirwJVsK5x1fVQds7Daa9nRoWtysB9mlV+K\n2IY2eB66m6I4GYTLlFJodQMoANGENuaJUtA0iVY3wOaaNdj/JgWFzjo+/v7zH/HisDN0bPzhuIMv\nX5zh4U4Vv/hg+1I3qjfXbMRxMvYcANByh6vBcZ8norfhOqv7TJr7G5X0t/vVxztLu77iNRwR3QYM\nChEREd0RF11hcRMCNJtrNmxDm6tVzqTJsmWEjWh1TGqdNLrqGJi+KnQZn/O070TR6xkNMy379dBq\nW8bx7LKWddznan2i2VgmnzKLHjM3qhb+4y/ewT99eYDDhgc/jCEgYBoSFceEZQ63812vpm3Kjpoe\nyrYx0m7sTTvWVnd4XDKrLSpDzNev6KZYxTFQcgwYpo5m20ecJAAUzrsB6rU04JAFCOpVC3/103to\ntHycnPfweLcK+dE2vn7ZRC+IoRRwXwA/HHXQ7PSDY/3fNzQBTQpYhoQfZu2/ZH+/mxzSAdJ9ZdZ+\nPvpvSin87svDieMi0W9Ftn/cga5Nfg1seXq7XPTmrK7JQbis64X970m6eKRweymgawJxkiAIE1im\nBqC4Kqzbi/DioI0gjAsfLYwTPHvVQhDF+PXHuxc+tzc7AYIo6VfhGpdVg3v3XgU/e2+L+zwRXauL\nzD1PO6bPcpm26ZfBazgiui0YFCIiIroDLrPC4iYEaER/4nfaxSgwfYJ4FW7O0/JMap00uuo4UxQU\nWtbnPO07VHEMGJpEmHs9k25wcL+7G5ZxPLusm3DcJ7oNWCafLksIgfNuONcND9OQ2FyzYGoSpi5R\nsY3CtsJFIeZpbVF5Drh+hTfFBHC/XsLheQ/3Nhz0ghh+ECMIQpi6hu11B2VHhyYlPny4ASnlWCjn\n/mZl6Ji0Vjbxx29PESVvxqlCALWyCdFvbef7UdqWSddmBoUusq/MMy4q2TrevVeBoWuFFUJ5w+7m\ny99EbnZ8vDrpwjS0oWPXPDdnszHubr2EL79vDH4uCnYcAWCjZg+uG/0gHgSFxqpnKeDgtAs/TKBp\nk8fmCgoHpy6+enmGX1+gqkU2r+NYOva2yzhsuEPXkRldkzB0DesVc6HHJyK6jLnnnhOF3391iHe2\nK2h2A7zuH9PzlQOzY/onTzexsVGe+FiXbZt+EbyGI6LbhEEhIiKiO+AyKyxuSoBmo2rhs6ebhSs6\ngNkTxPNMQiul4PYilG0Df37eYFnZFTapDUZ+1XH+Ux5dFbrMEMbU75AAduol7B93oaAGq1xHcRX0\n3XLZ49ll3ZTjPtFN9jbL5NPtEkTzjfGBdBxkGBIPtsow+ze8RxWFmCe1ReU54O2YdFOsZOt47NSw\nf9yBEAK2qaHdVfD8COfdAEEY42fvb00MD4yOP0qOgQ8eruObH5qI4gSaFGkoyEj3nbKtw9QlNJlW\npCoK6WQus6/MMy762Xvp38UWILdPvmqD24uGruPy1dCA2TdnszEuANSrNg7CLuJEQdclgvjNviWF\nQNk2sFE10Wj5iBM1VsErr+OF6IXx1MqwmTBOcHreu1BVi/y8TsnW8eR+DR0vRMcLkSRqLPy5rMoZ\nRETzmGfu2e1Fg5BjsxOg2QkGVd3yrROB9Jj+x29PsLbmYHOt+Fi2jLbpi+A1HBHdNtpvfvObt/0a\niOjm+Q0A9HphYbldIlo9Lw876PTCubeX/ZLwQHpDoewYOG56Ewpyv/mdjx/X4ViXyyFLKeA4w5PX\n8x5vspV1VceA7E+O10omHu9W8ZN312e+NsfSUSubOO/4iEaez+1FOGp4KNnppKDrR+j0Qhw1PRw2\nXJQd49J/Oy1Po+VP3OcNXcKxdLh+NLi4t019sFLeNjR8/Li+tBDGrO9Q9np6foSdemlw8yWz7NdD\nqcsca67DZY9nl3Hdx32i22zSseb4zMOrxnyVu6JEoeoYgzEIUd73r9tojrQLm2azZmOjak++NhCA\nZWhou2/+3cmNkzI8B7w9Bw0Xrh+N/VwKgXLJRH3NhlIKR2cevF4EXUuD8msVCy03mHrtMjr+qFct\nvLNdRnY/LCsutFG1sLddRr1mo+wYg5t6RZaxr8wzLhJCoGQb2Fp3cG+jhK11ByXb4A26Gyyr2hDG\nCZRS+PG4O3RzNlEKbTeEY+kw9LQNogJw3vGxt10e++zzY9xeEENKAV3XoEmBMEog+5WyHFNDveZA\n1yV0TcIPYxiaBsvU0nDSxnAw7qztQ0DMvHGcfx21srlwiGdsXkcgraxUMlAtm6iUjPRasv/a8vM6\ny7Tq11FE9HbMmntOw55dxEoBCjht9WAYb1rYpsf0YOiYLgTQ9iI82q0Ojun54820ub8itdLix968\n0/Mer+GIbqGRsc1/eZuv5brxap6IiOgOuOwKi7dd3WIRoj8ZdtELv42qhV99vDO0GtXzY3S9EO/c\nqxSulGVZ2dUzq3VSugKziq4XoeOFePdeBZs1+8pWHc/6DtWrFn798Q6SRHEVNA1c9nh2GTfpuE90\nE72NMvl0Oz3ereL7w/ZC29drs8dJ+bY2o9UzeA54u3RNTv1314twct5DtWTC0mVaYSQX9Jp17ZIf\nf2TVXPa2K1irWIPKJUoBcazwFx/egxDiWsYLb3NcRNdvtGpD14sK5zUUFA4bLp7crw2u1UerJOdl\nY1w/iNByA9imBtvU4Fg6mm0fclA5K/2emYaG9YoFTUoIiLHqWbah4dFOFd+9as1d4S1J1NxzNPm2\na9/uN9HxorGWkZNctnIGzZb/fHgNT3fd1GOOAg4b7qB6kB/Gha0TFdJA9JP7uWCQH+HozEuPvyOu\nu206r+GI6LZhUIiIiOgOmDWZPM/2RQGa2zoJkp+EVkrhd18eouxMXwHCsrKrZZ7WSUIIVEoGttbs\na/nc5v0OcRKBVsVdOu4TXbfrLpNPt9f776zhn/71CG4wXmFmVMnU8f47a2lbqhnjpKytTRjFeGe7\nkrbn4TlgJUy7KaagsH/cQb4k4GjQC5jv2iWr5pIoBQigUjLGKkv96XkDnz3dfCvjBd6gv91GW9h0\nvMkVI8I4QccLh/bPaTdnN6oW/re/eAf/9z//iNOWn7btKgnsbDhou8FYdeGSbeCjd9ewUy8jjJKx\n/e3rl8252o5lpBRzzdHk264BQMdLw00tNxhru1Zk0XkgWszo55M5OHMZqKU7adoxp+OFQ8EgP4gn\nnqujOEHXi4aO6UcNtzAodN1t03kNR0S3DYNCREREd8CyVljcxVWc8/TYzkxbuUjXSwiBDx9uvLm5\nMYHsb3ddNxPu4neIbjbus0RXYxkhbiIAkFLiP/zsPv6v3/84c8zzH352H1Km+9Jc4yQp8MuPdnij\nc8VMuynW7oYIozc3pXRNoOwUT/9Ou3YZreYyST5wdF3jBaUUnr1q4Y/fngzaR2UVVqbdoGew6GYZ\nrdoQz2hrNRoUmnVzVkqJX360M3Yc3F530PHCQfUsTRP45Yf38PRBbeJ+srVmo+IYaLnztYGsOMbM\nqhZDQb3c72XPEcYJ9o+72NsuTwwLXbZyBk1W9Pnkseo03UXT5p5Hw56JUrDNybenR4/p+bFN3ujc\nn1JqUDk8ThS0/hihWjKWMvfHazgium14lCIiIroDssnkeSxjhcVtcpGysrQasrLyk/Z929A4cUdE\nRG/FojfveLOPpnm8W8Pf/vIdlCbccCmZOv72l+/g8W5t8DOOk26u7KaYLLjZ1c4FFYQA7m+Wpt4U\nm3TtcpHFEtfhrO3j7z//EX//+Y84afXQ6YVouQFenXbx/HULbi8a3KA/a/tDv/e7Lw/xp+8bODhz\ncdLq4eDMxZ++b+B3Xx4ObUurYTToM6tiTzISJJrn5mzhcbBfPWt3s4SnD2r4T3/5EO/trU39Hm2u\n2disWXM9p6HJdPsp5/VJQb2yow89R9Z2DQVZFc7rXJ1Fg5RqxnZEt8W0uefRY7ShS1jG5GPmaDjU\n0Cdvmx3L4zjB89dtvDrtouUG6PbHCMdND1G8nO8hr+GI6LZhRSEiIqI7YFWrq9wELCt7s11V66Sb\nuCL5Jr5mIqLb6rrL5NPt93i3hof3Kvj2x3N8f9BGEMUwdQ2Pd6t4/521QSWhPLaYvLmym2KjbW/i\n/o0wQ5fY265AqGRqJZZJ1y4XWSxx1dWEzto+/uW7E7w47BTlIsYqrGSVjpqdgJU/bqDR0M2sij1y\nJEg0783ZZRwHhRD46FEd590APxwV758AICCwu1nCRzPmXCYF9YQQ2K2XsH/85jmK2q5xXudqseo0\nUbFpc8/5Y7QA8M52BWed4ZCuUgp+mMAPYwRhDE0K1MoGHMfEvYK2Y6N0TeJevypckqihioMQWMq5\nntdwRHTbMChERER0R0yaTM6wh3oxlpW9+ZbdOums7Rd+j6a1Onjbil6zUgrf7p8jCGPc3ypjo2Lx\nxiAR0TVhiJuugpQSHzzcwAcPN+b+HbaYvLlGAw4JAE3XsV61UC0ZEELAdadXypl07bJqiyWyCh5t\nN5z6XFmFlSf3a+iFMU7OPXy331qohRqPt6thtIVNVk1n0udfcd4EZRa9ObuM42D6fdyFoR/hxWFn\n7HUamsTDnSp+8cH2zGvFaUG9kq1jb7uCg4Y7eI58UGhVr0dvk1UMUgJcGESLu4p9ZtLccxb21DWJ\n3XoJJUtHxwsR9o9jQRij1Q0G4Wbb0NByA3S8EG6g8Jcf70IphaMzD9+9aMAP4sHrrdestHoX0opw\n+eBk3jLO9VOv4RQGrSuVUnh/bx2n5z1+B4lopTEoREREdAfkL/7Kto7s8qRk6zB07UonD276ZMW0\nHtuTtqfb66zt37gVyUWv2e1FQ5PLpy0fe9vllQ47ERHN66aMPRjiJqLLygccdF0iUAJ/+Ppo7t+f\ndO2yaoslsgoeHS+cuW2+wsqz/RYrf9xQo1UbiqrpZAxNDoJCbzNgu1G18B9/8Q5Omh6evWqh0W9p\nV69aeLpXw9aaM9frmhW8K9k6ntyvoutF6b7u6NjdKK3kWOc2WrUgJXD9i5luylibJrvKfaaoUtvO\nhgPLkNB1OdhHduol7B934YcRmh0f2ZSVJiXMrC2ZAO5tOPivf9hPf1+TcF1/ECg6OHMRhjHCWKFk\nz77dvYxzfdE1nNuLcNhwEfa/D7v1Erp+iD993+A1HRGtNAaFiIiIbrlJF39A2sr+w4fVC1+szJoc\nuImVV0axrOzNcdWTVdlK5pu0IrnoNbu9aGyCfXT19aqFnYiI5pWNPbwgGtxAixMFTQps1iz8xYf3\nUK8t91w97fwzC1s/EdEy3dtwYFs6en40c9tp1y6rtlgiq+AxrY1aXhYUarR9OHPcOMw/D4NCq6Go\nakNRNR0BgZ16CRCrEbAVQmB7o4TtjdltciaZJ3gnhBhUztjdKOGjR/NXkqPLWbUg5XUvZhqb51NA\nxwvw2z/3EIQxttcd7G2V8fRBDVvr84Xj6Hpdxz5TVKltt14eO6Y/2CrhT88ag5CQAFArmxBCwNAk\nHmyVAQAvXp9D1yR+8nB97LlOWz7aboC97cpcYaFlnOvz13DPD9p4fdKFY+nYdgyUHX1ov+ccGxGt\nMgaFiIiIbrGrvPibFQLa3Szh5WHnSi88r2MVE1uD3AzLDqUV7VtSAL0gBub4iFdlRXK2+jqjlMJB\nwx1bhQsMr75epbATEdG8snFPxwuHbiJmWm6A/ZMu/tef7+Hxbm1pzznt/PPJ001sbJSnPgZbPxHR\nsggh8Nl7m/j9vx5BXeLaZdUWS2THc03ONy5N5gwUTXoeWg1FVRvy1XRuawvlVQvq0bBV+nyUUvjq\nRQOtbjAUjq+MhBWWdX0/Osfo9iK8PGyj0fIRJ+nx8/jcw6vTLv784gyPdir4xQf3GI5YIW9zAVzR\nMV2p9Od+GCOKE6xXLDiWjopjoOIYkBLpQjcFhFGCdjeEPvJykkRBAThouHhyvzrz9S7rXC+EwOaa\njW/3z3F/a/r1HufYiGhVMShERER0S13lxd+sAJIXRPiHP+zj3kZp6mqOy1woXWe1IrYGWQ2TgmFS\nCnzx7BRtN+0FniQKsj85VnGMhUNphfuWAp6/bqHZ8ccmLoqCQ0opfLd//taqQ2Tv1effHOP4vDeY\nLFRKTZ0UyYJCwOqEnYjodriuqm8dLyxsS5IJogT/8Pk+/vf/YF66stA8gew/fnuCtTUHm2s8lhLR\n9dhcc/DLn97DF9+dwnX9sX+f59pl1RZLZBU5Ko6BlhvM3F72A0X1qgVvztZj+eeh1XEXK++tWlCP\nhq3S5/PsVQv/+rJZGI7P2h9lc3KXvb4fnWN0exGev27hrOVD5UbeSgGtbgBDl3j2qoUwSvCrj3c5\nX7YiRheTTXMVc0Kjx/TnvRZqZXPi/FrHCxFGb/bvthtgo2wMbZOd86M4QdeLBnNakyzzXP+2308i\nostiUIiIiOiWuqqLlXkCSF0vQhAlg1ZG0yqwXORC6bpLKwN3c4JylUwMhjVcfLt/jihOBpMDmZYb\nwNAkdvqTY/lQ2rTQ0Z+eN8ZadR02XByfe/CDGF0vxHrVHnv8/PYHjTSw9mD7zaqi62q5l3+vTs57\n6PbCwfvR9UIYuoRpaIW/O7r6mu0XiGgZriPce3regxdEE6um5QVxgn/++gh/+8t3L3z+njeQrZTC\nF9+d4m9+vsexAhFdm801B3/z8z382/enODx1L3TtskqLJbIKHmVHh67JmdUAKo4B29Dw9EENf35x\nttDz0Oq5a5X3Vi2oR8OW+flcJkh/1vbx+6+PJh4PozjB/nFnqB1Tdn2/yPNm2363f47vXrUGi5AO\nz1ycd4KhkFAmThT8MIFtanh16uKrFw38+pNd7qsrIGvlucj2yz725o/pUZzAaU1+TR0vHPrvOB7f\n3/Ih4vzit0mWea5fhfeTiOgyGBQiIiK6pa7qYmWeAFJ2IZdvZbSM5wbebpncuzZBuSqmBcOOmx4O\nTl1AKKxXrLEATBgn2D/uYq8f2Dk970HT5PAND5Xus//z22McNjys9UvXlx0dnh9j/7gLBTXYjxTS\nFXKba9bQ45dsHW4vGlSykNb4UPuqe5OPvlej4akwStDthYXvVdH2bL9ARJd1XeHek/Meul4093Hr\ntOVfakVn4Xiofz7JV7erlU04pRBHZx526qULPRcR0UUIIbC97mCjcvFj66oslshX8Nitl6ZWjjM0\niWrJwIcPN7BeMWG/aq1E5Q+iRaxSUI/GLePzuUyQPpsXKwpNDG2H4XZMUZws9LyDbYMYz1+3cHre\nQ6IUoljB8yMIoWDoxYuQ/DCGbWqI4uTS425ankXneK56TmhWdZ/RxWyaNj7uqDgGDE0ijBPEM1qP\nLvtcv2rvJxHRohgUIiIiuqWu6mJlngBS/sJsnqDQIq+VZV3vlqnBMAX8eNxJV7CpLLxTsAIOalDd\n6vnrNrwgGiqXfdhwEcYJ/CBGyw3Q9gJ0vAAly0AQxYPwjGVo6PkRACBOEgRhAsvUBo//eLc6VMmi\n4hTv91fVm7zovRptzyCFGJQCL3qvRl8z2y8Q0WVcZ7g36oeT55Uk6lIrOkfHQ/nzSV7HC3HuhqiW\nTAaF6Ea76vaBtLpWYbFEvoJHydaxt13BQcMdu44UEHi4U8W/e29rcKOblVnoplqVoB4Vu8znc9kg\nfTYvNrrQp0i+HZPnx3M/LwB88ewUHS9MKyw3Pfj9ubieH8PtRYhVglrJgGWO32ZUanhekJVUVsOi\nczxXPSeUVQycREoB5AJx1ZIJjEaFBbBTL2H/uAttynfiKs71q/Z+EhEtikEhIiKiW+qqLlbmCfXk\nL8xGV39c5rkBlnW9a6YFw0Z7lefLW48K4wRtN8Bx08P2Rro/pNV/uoNS2X6QPk8WpEkShZYbDKrv\nWIaEJsUgCOcHMaz+c4VxgqOmN/h+GJqcGBQCribEVvRe5VdWAYBlavCCqPC9KnrNbL9ARIsYDRJ0\n+zcFKo4xtQ0pcPnjoq7JmStI86QUl1rRmf/d0fMJAEClK6mDKEGnF+G//WEf2+sOPni4ceHnJHpb\nrqN9INEs+QoeAPDkfhVdL0LHCxEnCo6p4Wfvb+Hpg9rQTUBWZqGbbBWCejTZRT6fiwbp8+Ps569b\nOO8GkCJ9vFnBh44XouzoaHZ6MCa0Ic8/71cvGoAQ6HjhYIwrc8+hBv8HtNwQ61KMVRbKv6Y4Uayk\nsiJmBXOKtr9K+YqBRSqOgV6YLn4zdIlq2YCXWwyXSUPEZRgFFYeAqzvXr9r7SUS0KAaFiIiIbqmr\nuliZJ9STr2IyzwqnRS6UWNb1bpkWDOt44dBkFfCmvPWkx3KydmAKOGy4Qzd18xN1caLQ8cKx6ju1\nsolmx4dSGJvYOz3vQdclBERaNWLGrr/sEFvhe5VbWaWgYBoSmpSIk2TovSp6zWy/QESLKAoSHJy6\naLkBDE1ip15CyZ4+BbHocTF/w+S01cN5x0eUKFiGnHnDpOIYl1rROfjdgvNJECZodQPESQIhRHpu\nEMD/+Y8v8Oqki0fbZVSnhEmJrtoi1YGKqh4opYYCGi8O2/jlh/fGAhpEy3bRCh6szEJEq+Lk3MPJ\neW+oVW3FMQqD9VmQfrR9eqPto9sLAQU02wHKjgHTmDyujROFKEomtgkbddryAaVwfN6DUmoQfvf8\nCAIC+Wk2hTQ0XysPj7+tXCBJk4KVVFbErGBO3nXMCeUrBhaF5yqOgXM3Qhgn2NuuQEyZaKtXLfzV\nT++h0fKv7Vy/au8nEdGiGBQiIiK6pa7qYmWeAFLZ0aFrElGcTK2qsuhzAyzretdMC3oliRpUyMmo\nKavyur0Q2/0b0B0vHGsPUxQ6yipUZNV3TEPDesVCqxuMbR/HCo41383wWX/bRUx6vGxlVdYSp1Y2\n0Wz3Bu9V0Q18tl8gokVMap+QVfgJ4wT7x13sbZenHh8XOS6OBZMUIARw3vGhyTTYaU5YMZ1VULvM\nis5sPDR6PgnCBM22P1xdCIBt6gijBMdnHprnHj55XGf1ihvoqttvXUd7r1nVgT54d33Qmi+MYvzb\nj+cwDQ1lR4cQAm4vKmz59P/+8RWOmh4+YmUWumIXrbDCyixE9LadtX38tz++xklreJHPtGD984M2\nPD8aGmcPqngLoOwYaHb8fiXk4vkvXRNYr9ppuGgOHS+E50fo9qJB+B0A4gRIVIwkAaIkbQEvZPq/\no1jB0MXg9Vm513LZcTctz6xgTuY654SmVf4TQuDp3hqCMEbJnjy/nL1eKeW1nutX8f0kIloEg0JE\nRES31FVdrMwTQBJCYLdewlHDnRoUusiFEsu63i3Tgl5SiqEKOQCm7kt6rrVWxxufIBsNHeVXKuWr\n75j9cNt6kl46RAAAIABJREFUxYJS6Y1wTQpYNYmNmj2zktA8f9tFTHu8kq3jyf0aOl6Ylh230xvW\nDzbLYysX2X6BiBYxrX1CvhWpgsJhw8WT+7WJx8l5j4uFwSQBPNqpotkJEMVJ7obJSBuEfgU127zc\nis5sPHTg5cYk/Sp0oyEhTcrBOaTdDbBRMYZaSdDNME/7rfWKeeGgz3W095oU6ss02j7+j//+HNsb\nDpQCjpsejs89CCFQtnXcW3dw1vZR9NthnOD0vIcvnp3is6ebHEcQERHlZOdgLyieSysM1ivg+9et\nQfv0TL6Kt2lIrFcsdL0Qhm6OjTcMTeIvPtjG4ZmHg1N3ZhUjIJ3jaHZ8dL1oaFzrmBpcP4GU6Xxe\nGCfQIKBEGvg3+lU0a2UTANALYkRxArcX4vjcAwBWcVsBq9iSc1Llv53NEn7yeBONVg9ffHeKnh+N\n/e7bnsNaxfeTiGheDAoRERHdYldxsTJvAKniGPj4F3s4OHWXeqHEsq53y7RgWDY5NqiQg+Hy1nkC\nwAfvrA8mwZJkfN+1DG0odGQaclANY7RSkaFruLfhDE1wPaiX8Krx9kJsM0N0AqiUDFRKaVjqk8cb\nEBBsv0BEl3J63pt4Ts7fxADSGyAdLxwch0bNc1ycFkwqOQY+eHcd37xsIkqSodaRwJsKahXHuPSK\nzmw89Px1a/AzP4wH55DBdgDWK9bgubJ/z1pJsLLFzTArYNMLY/zuywMYugZNG96v5gn6jD7+aGsv\nTQqctnr46092Ua9dbPww7bsDpG1D9o+78MMYB88a2KiaaHdD+P0bmj0/wg+HHWxvOBMXAmTfbwbh\niIiI3sifg/NB+rHtRoL12ThgVL6KN5DOXZi6hfWKiURhKAxkmxqOmx5+OOoMjcunVTGSEmi7IfKn\n8ShO25cJIRFFMTQpkEiBOFHQdQEFDKp6Auk1QpIobNRsVEpmGlRquIiiBOtVG46pcQ7iLVrFlpxF\nlf/SFs4Cm2sO/ubnezg68/Ddi1P4QfzWX2/eKr6fRETzYFCIiIjolruKi5VFAkiPdqpLfW6Wdb1b\npgXDKo4Bo199Yr1qo+OFQ+WtM7om8Wings/eq+PP358BSKsRjemvfMtaxlQcsz8xlwztRwLAbr00\n9DPb0PD+O2totP23FmJbNES3teYMJmKIiC7q5Lw38d9Gb2IAmBgUmve4OC2YBKTHwp+9v4kXh210\nvQi6lCj3V0xnN0uWtaJzo2rh/b11fPH8FFGcDAIVGU0KrFcsWKaW+9mb89QJg0I3wqyADZCFbDrQ\nNVlYNasXxhMr7Yw+/qTWXi03wN/99gX+818/ulBYaOp3RwGHDRd+GA/C136YDP3NYawQJQkOGx6M\nbQHT0OCHCfwwhlIKQghoQkApxSAcERFRTv4cPBqkHzUI1jsGjptpFZ5Xx91B8Kds6+j2IkghcN4J\nIJBWR7YMDYkCdjdLg8fy/Cgdo0bJ4HmVUkPn79NWD492qthafzNHJ4Xot2FPEMcKXhAjUW/GJZom\nECdpSzMpAbNfvXlzzUYYpZU9pZDYWrfxcKeKkq0PjW/EQWdQOWmZlRNpMTetJacQaWVYU6ShtVVz\n095PIiKAQSEiIqI74SouVuYNIF3Vc7Os690wNRgmgJ16CfvHXViGxJP7dSiFodX3FcdAtWTg3723\nhfWKCdtooRfGEyfnTENivZqW7bZMCSnT4FBWqUjXJHZHVtzle6G/zRAbQ3RE9DaMhhnyslak+8ed\nQdOCoopuixyXpgWTMiXHwE8f1dHxQpi6xIOt8pWt6Hxyv4pOL0DXixCEMRKkgQnL0GD122PmVcsm\n0H83pr13tDpmhdOUUjhouFCYXjUr6QeCRivt5B8/CxxNOou7QYTf/vkA//7T+wuPc6d9dzpeiCCK\n+63zUn4YQ+ZeZ7a/JirBcbMHy5AY/TqfKeD56zZ26yUG4YiIiPry5+CiIP2o01YPx00PjbaPiqMj\n7G97ct5D1wtRdgyYhoRjaWh1A3hBBE1K6PqbcadlaIjjBFp/cVXZ0REnCmet3liVon/78RxtN8Tu\nZjrXoUuBjaqFw4YL14/G2uoCgCbTALwmBB7tVPH+Xg3NToDnB22ULAPrFQNlx0CrG+Cg4aLR6sHs\nj48hMFQ5aVqgmoiIiK4Og0JERER0YW9ztQTLut4d04JhJVvH0wc1AGowAZa/OTcaGsuCNFk1orBg\ncs4yJJ7e30SiFDpeiLWyiZKlwTJ1lB19rJJQ/vHfdojtbT8/Ed09ujZeyS2vZOvY264MVhCPVnRb\n9Lg0d7im325xq2bj06eb8/3OBWyu2XDM9NywteZMXSFu6BLVkgHPS7eZ9d7RapgVTut60VxVs4Di\nlnPZ4+cDR9O03fBCrb2mfXc6XoggTIZa5ymlYJs6vCDKfgAgDfu1uyFQNmDow/uwZWqI4gT7xx3U\nOdYgIqJbRCl14fmn/Dm4KEifF4QJzjs+1isWNms2gige/Dyr+he2E6xXLZj9ipxZhSCvF6JsG3iy\nW4VSCn9+cTZ4XM+PkcSqMLQfJwm6vRD7xx28e6+CB9sV2KaOl4ftwpDQ4G9Beu7v9kKUSyb8SGG9\naiEIYxw0PMSJC00KRLGCEGmFo3x7svyYaVKgmoiIiK4Og0JERER0Y7Gs690xKxgGYK5Ju3yQJqtG\nlJ/4Gq0YVCub+OzpJtYr5tyTgm87xPa2n5+I7patNRsHZ+7UbUq2jif3q+h6EbbXbJQd48LHpUXD\nNVcdxslXc5vWSkIIYG+7MvS3bi25BSVdjVnhtI4XDv130Q24vNFKO9njjwaOJkmSi7X2mvZdSBI1\n1jovq4ylybT1CIQAFBBGCaQQiOJkKCikSTmowKgAvDrpDlqSERER3WRnbb9wMc68bbNGz8GjQfoB\nBbS6ARxLw952BUmi8Oo0PZ/mq/4ppP+91R9H26YG29TwYLOMOE6wuWbj65fNNw/bDyMbhsR61Uar\nGwyFgwHAD9LKy4auYb2czn9sb9g4aniICyoWy/7zCiEQxgqNlt8PHsdodvwsX4xemKDnRyhZer9l\nmUKzH4QaDVezdSkREdH1YlCIiIiIiG6EWcGweUNj+SDN8/U2vn/dQpwoVBxjqGLQ6ITfIqG0qwyx\nzbOScdrzX2YlJBHRqM01G7ahTW3NBPSPS2s2/uqSq4TnCSaNbn/VshDqVy/PcNz0xqrVGbrE3nYF\nZWe44t0mg0I3wqyw2Wj7jtGqWaNGw0DZ448GjibJHn/R1l7TvjtSirG2pZahASINTTfbPnRNoudH\nUAAExFB9AYF+dYDcn24aGm/2ERHRjXfW9qe2956nbVbROTgfpM/apwdhjEoprQhUsnUopaBrEl0v\nHAv2xElaRcgy37RJLzv6IGyTH2/kw8imIbG5ZiEIE/hBjEQpSCGwUbMGrcA0KdIQj2PC2JZotHz4\nYdwfA6TPpWsCmiYRxwplx0DHCxHFSRpoyr1VUZxAQcELYlSc9Hak6geiNirj7xdblxIREV0fBoWI\niIiI6M7JB2l++eH2jQnOXHYl42V/n4hoVL6izqQbKEC66vjDhxuXPq7OG0wCrjeMs1G18OuPd3Bv\n3cHvvz5CFKt+awUD9zaHKwmJJb0XdD1mhdO0kWBQxSluO5YZDR5ljz8aOJoke/y52/D1TfvuVBwD\nMrc/alLAMtLXaRoS61ULra6Pdv/fpSYGmSBNStTKJkzjzd9laBIVx+DNPiIiutFUvx3WtDEuMLtt\n1qRzsBAClZIxqKpzcJq2IM3+O2tT9uX3jbHXFcUKjVYPjq1DCoFHO9XBv5+c94bGG6NhZCEELFMb\nhIwAoGwbg8BvrNRgfGOZOnY3tUF7s6xaoGVoQL9du2VIJIlCGCXj45msdalKEMUKupY+bpwoBNH4\nWGbR8Q0RERFd3NXW4CYiIiIiWnFZaOijRxv49OkmPnq0ga11Z+Vu4GYrGSfdHM9WMp61/Sv5fSKi\nSbKKOrahFf67bWhTV1kvIgvZyBnH6GUFkxYhhMB7e2v4T3/5EO89qGF3s4RqyRx6Dbal42fvbzGU\neYNkN/cmyQeDsoDMNKNVrrLHHw0cFck//qJt9aZ9d7Kqiul2aRWh/H5rGhJbaw7WyyYMTcIy0tex\nXrGwtWYPhYQEBHbqJUDwZh8REd1sp+e9ucLpwJu2WUXmHb8qpbBbLw2dg0u2jnrVHowTojhBtxfB\nCyL4YYwoUrBNHWcdH89ft+H20upB+fHGPGHk/PgljhUe79YGoeCsvdla2cR6xcJa2YRtagiiZDBm\nmFhRMfe3RAXBoFFX3TaYiIiI3uBZl4iIiIhoxS26klGNbHfZ3ycimiVr6/jp4zp2N0rYqtnY3Sjh\n08d1/OrjnaUGY64zmHQRQ+9FvYR7GyXsbVfw8w/v4W9+vseQ0A0z6+Ze2dGha3IoIDNJUZWr7PGr\npekBo9HHv0hbvYnfHQE82qnCMjSsVyyYRd8tAZQdA+/eq2CtbGFzzU4rEeT+XkOT2Nsuo2SnoSPe\n7CMiopvsZELw5yLbzzN+fX9vfdByrOOGODh1sX/cRS+IUHEMOJaGsF+10tA1VBwTm2vWILAbxQn2\njzvw/Hgo6DwrjDwadNY1iSf3q9jbrkw8l+uaRL1qD8YMFceAaUhoUo5tl1G5xqWalIXjjetoG0xE\nREQpth4jIiIiIlpxF1nJmG/1cdnfJyKaR76t41XLwjir2joyey92t8rY2Ci/1ddCl5fd3Ctq3ymE\nwKOdCoIogWNNnmabVuVqo2rhrz/Zxd/99gXcIBr7d0OT2KmXBgGcy7TVm/jdeWzjp4838F//8ApB\nQSUgQ5N4tFNFs+Nj790Kkn67kSRRkFKg4hjpTcbcn8ebfUREdJMtWhlv1vazxq+n5z3801dHOGi4\nQ48VJwrn3QBuL4JpSOhaGrCployxcYUC0OykgaWsPXDFMdByg8LXVBR0zl5PvWrBsTR0vQgdL0Sc\npCGlrBLhYcNDyw2gSwFA4byTPkcviKFrEromYGgCvhBIlELWuFRAoFY2xwJM19k2mIiIiBgUIiIi\nIiJaeRdZyZi/UX/Z3yciWkXXGUwimnVzr9kJCoNEQHrj68OHG1OrSdVrNv7zXz/Cb/98gLY7OYCz\njLZ6k747W+sO1soW/vnrI5y2/LHXYJka6jULWr86QGVKFSTe7CMioptu0cp482w/bfwqpcDRSEgI\nSFuAJkohShLEfhpMtgwdVkFFHkOTMHRtsPjns6eb+OpFA0dNb+xxR4PIwJvzd1bx8Itnp6iUjMJz\nfsUxcNrqIZECr05dRIlCnCQwNAkviAAIOKYGx9Th+iF0Pa04VCubMI3hKkZvo20wERHRXcegEBER\nERHRirvsSsZlr4QkIiK6i6bd3FtGlat6zca///T+pQJHl1Wv2fjbX747NRD1xbPTqe1MebOPiIhu\ng601Gwdn7kLbX5RSCt/80MS9egn7x92hNl1CCJi6BoEQCoAfJNhaM8banearA2WLfzaqFn79yS7u\nbZTw+6+PEMeTKwGOnr+nVVQEANtMqwZl21uGHFQJqjgGwlghjBOUbQMlW0elZECXEhBpqKrsvKmU\neNXjGyIiIhrHoBARERER0Yq77ErGq1gJSURERMOWUeVqFdrqzQpETb1pyJt9RDSFUgon5x6e7bfQ\naPsAgHrVwtMHNWytOwwYLkAptbItWG+LzTUbtqHN1cb7spX0snbhJVvH3nYZhw0XYW4BjyYFSpYB\nP4xhGRpG47qj1YHyi3+EEHhvbw31mr3w+XvSuGSzZuHbVy28e6+K/eMOVP95amUTzY4PpQBDEzA0\nDQLAp0/qKNkGOl6Ibi/Eo50q1isW91kiIqK3iEEhIiIiIqIVd9mVjNe5EpKIiIguZ9Xb6q1CmImI\nbp6zto/PvznGy8P2UADih+MO/vziDI92KvjFB/cYNJzDWdsvDHwcnLkMbC5Rvv3WVVfSy7cLL9k6\nntyvoeOF6HhpO9IoSluOmYZEECbQpEDFNiZWBypa/HPR83fRuOSk6cEfBJsqOOi3TDMNDesVC61u\ngDhR0KREtWwiUQBEOtfwv3x2n/snERHRCmBQiIiIiIhoxV12JeN1roQkIiKi22/Vw0xEtFrO2j7+\n8csD/Hg03FIpE8UJnr1qIYwS/Orj3aWECG5rxZ2ztj81uNILY3zx7BSfPd1kGGMJrquS3lj7bwFU\nSgYqJQMAUCubeHXaBQBYpoaybeDBdnni401a/LOs8/d4sKmKrheh44WIE4XNmg0pBRKlkCSAqUt8\n+rh+479/REREtwmDQkREREREK+6yKxmvcyUkERERERFRRimFr16e4eDULQwJDbYD8OrUxVcvGvj1\nJ7uXuia5rRV3lFL4+uXZ1Gs6AEj62/3q4x1e2y3BdVTSm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e92cf2be0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 794,
       "width": 1157
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(bootstrap.index.values,\n",
    "                 y='redcard',\n",
    "                 data=bootstrap,\n",
    "                 lowess=True,\n",
    "                 scatter_kws={'alpha':0.4,},\n",
    "                 x_jitter=(0.125 / 4.0))\n",
    "ax.set_xlabel(\"Skintone\");"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
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   "source": []
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